Soil microbiome indicators can predict crop growth response to large-scale inoculation with arbuscular mycorrhizal fungi

Alternative solutions to mineral fertilizers and pesticides that reduce the environmental impact of agriculture are urgently needed. Arbuscular mycorrhizal fungi (AMF) can enhance plant nutrient uptake and reduce plant stress; yet, large-scale field inoculation trials with AMF are missing, and so far, results remain unpredictable. We conducted on-farm experiments in 54 fields in Switzerland and quantified the effects on maize growth. Growth response to AMF inoculation was highly variable, ranging from −12% to +40%. With few soil parameters and mainly soil microbiome indicators, we could successfully predict 86% of the variation in plant growth response to inoculation. The abundance of pathogenic fungi, rather than nutrient availability, best predicted (33%) AMF inoculation success. Our results indicate that soil microbiome indicators offer a sustainable biotechnological perspective to predict inoculation success at the beginning of the growing season. This predictability increases the profitability of microbiome engineering as a tool for sustainable agricultural management.


Most important soil parameters for MGR prediction (extended)
The 15 soil parameters in the full model could explain about 26 % of the variations in MGR (Fig. 4 A).These comprised cMIC, Nmin and magnesium.Total nitrogen content was negatively correlated with MGR (Supplementary Table 2), while SAF22 was positively associated with MGR in the model.A higher content of easily leachable magnesium (H2O extraction) was also associated with a higher MGR, while this was reversed for more metalbound and thus less bioavailable magnesium (EDTA extraction).This could be due to the fact that magnesium plays an important role in stimulating root colonisation by AMF (Gryndler, Vejsadová, and Vančura 1992) when bioavailable.

Most important soil OTUs for MGR prediction (extended)
Analogous to the soil parameters we reduced the number of candidate sOTUs for model input, again using different approaches (Fig. 3 B).An indicator species analysis revealed 11 and 20 sOTUs characteristic of fields with high and low MGR, respectively (Supplementary Table 7).Differential abundance analysis between fields with high and low MGR resulted in 5 and 14 sOTUs, respectively (Supplementary Table 8).Furthermore, a random forest analysis was performed on the continuous MGR values (Supplementary Table 9).Only the top 30 predictors were retained since this number of sOTUs corresponds to the identified number of sOTUs in the other two analyses.
Comparing all three methods (Supplementary Table 10), only two fungal sOTUs were identified as important by all three methods (sOTU18: Trichosporon sp.(high MGR) and sOTU182: Dendryphion europaeum (low MGR)), which was in contrast to the relatively large overlap of soil parameters selected by different approaches.In addition, seven sOTUs were selected by two methods and 49 sOTUs by one method only.For reasons of stricter selectivity and due to the small overlap between the random forest and the other two analyses, sOTUs identified solely by random forest were not retained.The combined results of the indicator species and DESeq2 analyses yielded a total of 44 sOTUs.
While fields with high MGR were characterized by higher pathogen load, the main indicators of low MGR included the genera Phaeohelotium, Phaeosphaeria, Powellomyces and three sOTUs without database match.Not much is known about these genera in the literature, but they could be indicators of generally healthy soils, as suggested by the positive correlation of the most important predictor OTU58 (no database match) with soil fertility and microbial respiration (Supplementary Fig. 13).

Model with all fungal sOTUs classified as pathogens not able to predict MGR
Since soil pathogenic fungi were the strongest predictors in the model, we investigated whether the relative abundance of all soil pathogenic fungi would perform better in predicting MGR.Therefore, we evaluated fungal guilds of all sOTUs using FUNGuild and summed the abundance of sOTUs that were classified as pathogens (Supplementary Table 13).However, their abundance was neither significantly correlated with MGR (R=0.029,p=0.83;Supplementary Fig. 20) nor performed well in a linear regression model (R= 0.0036, p= 0.6701).On the one hand, this could be because in most cases there are no exact matches in the sequence databases and because even within a species, traits are often highly diverse.Therefore, functional annotations are often unreliable if not conducted on the strain level.On the other hand, it may indeed be that only a few pathogens AMF strains are likely specialised in their ability to protect against specific pathogens.

Root microbiome data confirms findings of MGR prediction model
Of note, we identified Trichosporon, the most important soil fungal OTU in the predictive model, also in the data of the root fungal communities (rOTU20 shares 100% sequence similarity with sOTU18 over its entire amplicon length).Although the Trichosporon rOTU20 was not significantly (p=0.263)different in abundance between control or inoculated roots, it showed a trend of a decrease (log2FC=-0.583)similar to the differentially abundant pathogens in roots of fields with high MGR (Supplementary Table 15).

Inoculum application
The inoculum was manually mixed with field soils.This application mode is highly suitable for scientific experiments, but it is not feasible for large scale agricultural production.In order to achieve a better cost-benefit ratio, the dosage and method of application of the AMF inoculum must also be optimised, ideally in cooperation with farmers and specialised biotech companies.Comparison of the 44 soil OTUs selected (grey background) by the indicator species (31 sOTUs), DESeq2 (19 sOTUs) and random forest analyses (continuous ranking of sOTUs).Within the top 30 sOTUS identified by random forest, only 5 sOTUs were overlapping with the indicator species and DESeq2 analyses.The combined results of the indicator species and DESeq2 analyses (44 sOTUs, no further sOTUs of the random forest analysis where added in order to not inflate the final pool of sOTUs) were subjected to a further selection step with glmulti.The resulting final 13 sOTUs are highlighted in bold.In addition to the automated taxonomic assignments, these sequences were searched against the NCBI BLAST database to obtain a more refined taxonomic identification.On the left, the summed relative abundance of all root OTUs corresponding to SAF22 (rOTU2, rOTU4, rOTU9, rOTU16, rOTU74, rOTU84, rOTU165) is shown for the control (CTL) and inoculated (AMF) samples, as well as their difference (Δ).On the right, the averaged (eight replicates) total root colonisation of the control and inoculated samples is shown, as well as their difference (Δ).

Supplementary Table 2: Pairwise correlations of soil parameters and MGR.
Field Relative abundance of SAF22 rOTUS Total root colonisation

Supplementary Table 3: Overview of co-correlated parameters.
Table show Spearman rank correlations coefficients and associated p-values in increasing order.No significant (p<0.05)correlations were found.Magnesium, Corg, humus and Ntot were weakly correlated with MGR.

Table 4 : Random forest analysis of soil parameters.
The analysis was conducted on the reduced pool of 22 parameters (see Supplementary Table3).Parameters are sorted by descending importance (IncNodePurity).

Table 5 : Soil parameters selected for final model input.
Comparison of the most important predictors of MGR identified by the glmulti, stepAIC and random forest analyses.The selection by the respective methods is shown with a grey background.The glmulti analysis was limited to a maximum number of ten predictors.Similarly, for the random forest analysis, only the top ten predictors are shown.

Table 7 : Results of the indicator species analysis of fields with low and high MGR.
Displayed are soil OTUs that were associated with low or high MGR (Spearman rank correlation, p<0.1).

Supplementary Table 10: Soil fungal OTUs selected for final model input.
Table shows soil OTUs with differential abundance between low and high MGR fields (Wald test, adjusted for multiple comparison, p<0.1).A negative log2FC corresponds to a higher relative abundance in high MGR fields, whereas a positive log2FC corresponds to a higher relative abundance in low MGR fields.

Table 16 : Number of AMF OTUs (Phylum Glomeromycota) in the root samples.
Table shows root OTUs with a significant (Wald test, adjusted for multiple comparison, p <0.05) differential abundance between control and inoculated samples.A negative log2FC corresponds to a higher relative abundance in control samples, whereas a positive log2FC corresponds to a higher relative abundance in inoculated samples.In addition, the values for rOTU20, which likely matches the important soil predictor sOTU18, are highlighted in grey.Table shows the number of OTUs with the taxonomic assignment "Glomeromycota" for the control and inoculated samples, as well as their difference (delta).A positive number represents an increase in AMF OTUs as response to inoculation with SAF22 (44% of the fields) and a negative number a decrease in AMF OTUs (42% of the fields).The mean change in biodiversity across all fields is 0.46.